The authors of [1]}
proposed a framework for states abstraction via aggregating original states into abstract states to reduces states and action spaces. Based on the unified state abstraction framework in [2]}, the authors therein proposed and proved the existence of four abstractions approximate types that guarantee the gap to the reward using the true state value function. The developed framework therein was tested in five different problems which shows significant reduction in abstract state space and suboptimal value function.
In [3]}, a representation learning scheme was proposed for hierarchical reinforcement learning in which a high-level controller learns to communicate the goals to the lower-level policy that is trained to achieve the goals. It was shown that a good choice of presentation learning policy leads to a bounded suboptimality. Although developed for the single-agent system, these frameworks are useful to reduce the communication message in multi-agent networks.
